Key Business Analytics: The 60+ Tools Every Manager Needs to Turn Data into Insights: The 60+ Business Analysis Tools Every Manager Needs to Know
By: Bernard Marr (author)Paperback
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Key Business Analytics will help managers apply tools to turn data into insights that help them better understand their customers, optimize their internal processes and identify cost savings and growth opportunities. It includes analysis techniques within the following categories: * Financial analytics - cashflow, profitability, sales forecasts * Market analytics - market size, market trends, marketing channels * Customer analytics - customer lifetime values, social media, customer needs * Employee analytics - capacity, performance, leadership * Operational analytics - supply chains, competencies, environmental impact * Bare business analytics - sentiments, text, correlations Each tool will follow the bestselling Key format of being 5-6 pages long, broken into short sharp advice on the essentials: * What is it? * When should I use it? * How do I use it? * Tips and pitfalls * Further reading This essential toolkit also provides an invaluable section on how to gather original data yourself through surveys, interviews, focus groups, etc.
Bernard Marr is a leading global authority on organisational performance & business success. He regularly advises organisations & governments across the globe & is an acclaimed, award-winning keynote speaker, researcher, consultant & teacher. He's acknowledged by the CEO Journal as one of today's leading business brains.
Introduction The Raw Material - Data Data Types and Format How to use this book Part One: Bare Analytics 1: Business Experiments / Experimental Design / AB testing 2: Visual Analytics 3: Correlation Analysis 4: Scenario Analysis 5: Forecasting / Time Series Analysis 6: Data mining 7: Regression Analysis 8: Text Analytics 9: Sentiment analysis 10: Image Analytics 11: Video Analytics 12: Voice Analytics 13: Monte Carlo Simulation 14: Linear Programming 15: Cohort Analysis 16: Factor analysis 17: Neural Network Analysis 18: Meta Analytics - Literature Analysis The Part II: Analytics Input Tools or Data Collection Methods 19: Quantitative Surveys 20: Qualitative Surveys 21: Focus Groups 22: Interviews 23: Ethnography 24: Text Capture 25: Image Capture 26: Sensor Data 27: Machine data capture Part Three: Financial Analytics 28: Predictive sales analytics 29: Customer Profitability analytics 30: Product Profitability analytics 31: Cashflow analytics 32: Value Driver Analytics 33: Shareholder value analytics 34: Risk Reward Analytics 35: Unmet need analytics 36: Market Size Analytics 37: Demand Forecasting 38: Market Trend Analytics 39: Non-customer analytics 40: Competitor analytics 41: Pricing analytics 42: Marketing channel analytics 43: Brand Analytics Part Five: Customer Analytics 44: Customer satisfaction analysis 45: Customer Lifetime Value Analytics 46: Customer segmentation analytics 47: Sales Channel Analytics 48: Web Analytics 49: Social Media Analytics 50: Customer Engagement Analytics 51: Customer Churn Analytics 52: Customer acquisition analytics Part Six: Employee Analytics 53: Capability Analytics 54: Capacity Analytics 55: Employee Churn Analytics 56: Recruitment channel analytics 57: Competency acquisition analytics 58: Employee Performance Analytics 59: Corporate culture analytics 60: Leadership Analytics Part Seven: Operational Analytics 61: Fraud Detection Analytics 62. Core Competency Analytics 63: Supply Chain Analytics 64: Lean Six Sigma Analytics 65. Capacity Utilisation Analytics 66: Project and Program Analytics 67. Environmental Impact Analytics 68: Corporate Social Responsibility (CSR) Analytics Index
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